Focusing on strengthening the robustness of vedio object tracking, an algorithm via sparse col-laborative model was proposed. In the discriminative model, the prior visual information was exploited to learn an over-complete dictionary based on the SIFT feature, the dictionary was used to represent the object and train the classifier which separated the object from the background. In the generative model, it extracted the local feature and calculated the occlusion information of the object to construct the object templates, and then the tracking was implemented by computing the similarity between the candidates and the templates. Eventually, the multiplicative formula was exploited to joint the two models to acquire final tracking result. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed algorithm performs favorably against several state-of-the-art methods.%针对增强视频目标跟踪鲁棒性难题，提出一种利用稀疏协同判别模型和生成模型的跟踪算法。在判别模型中，利用先验视觉知识训练一个基于SIFT特征的过完备字典，用于构建目标外观模型和训练分类器实现目标与背景的分离；在生成模型中，提取目标的局部特征以及计算目标的遮挡信息来构建目标模板，通过计算候选目标与目标模板的相似度实现对目标的跟踪；最终利用乘性策略融合2种模型的跟踪结果。定性和定量的实验结果表明，与经典跟踪算法相比，该算法具有较好的鲁棒性。
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